Considering the complex nonlinear characteristics of chemical process faults and the underlying structural characteristics of samples, a fault identification method based on manifold regularized stochastic configuration network is proposed. Based on classical stochastic configuration network, this method randomly selects hidden parameters under the supervision mechanism of embedded manifold constraints to add hidden nodes one by one. Then, the output of hidden layer weights is calculated by manifold regularized least square method. It keeps the important geometric characteristics of data. The information redundancy is avoided and the relevant characteristics of different from different categories could be identified. Experimental results on test set show that the identification accuracy values of TE fault and semiconductor fault are 87. 72% and 84. 27% , respectively, which are higher than those of random vector function connection network and stochastic configuration network. In addition, for most fault types, the precision and recall rates of the proposed method are high. Results prove that the proposed method can effectively identify faults. The generalization ability of fault identification model is improved.